Sviluppo di una strategia per l’individuazione di squilibri di titolo tra cilindri di un motore a combustione interna

L’oggetto di questa tesi è l’individuazione di un metodo in grado di rilevare uno squilibrio cilindrico rilevante in un motore a combustione interna ad accensione comandata, sovralimentato dinamicamente. Il progetto si basa sull’osservazione sperimentale di un forte incremento dei valori assunti da indici basati sulla differenza dei tempi dente della ruota fonica, al manifestarsi di un brusco squilibrio di titolo in uno dei quattro cilindri. Ciò ha permesso di rilevare eventuali sbilanciamenti di titolo mediante una diagnosi intrusiva in grado di esaltarli. Questa metodologia a differenza di quelle basate sul segnale proveniente dalla sonda lambda, non risente del problema del mixing dei pacchetti di gas combusti all’interno della turbina. Il lavoro di tesi è consistito nel concepire un indice di rilevamento capace di esaltare il fenomeno sopra descritto, nel creare in ambiente Matlab-Simulink un modello che simuli la strategia in questione e renda possibile la realizzazione di un prototipo, per mezzo del quale è stata validata la strategia a bordo del veicolo. This thesis proposes a methodology to detect a relevant cylinder imbalance by means of flywheel speed fluctuation analysis in a turbocharged internal combustion engine. The main idea behind this project is the evidence that every time a significant cylinder imbalance is present, it is noticed an important increase of index based on tooth time sampled via flywheel. For this reason, it is possible to develop an intrusive strategy, which higliaghts a possible cylinder imbalance presence, in order to detect it. This method, unlike others based on the signal coming from Lambda sensor, doesn’t suffer from the presence of exhaust gases mixing effect inside the turbine. The objective of this thesis is to conceive a detection index able to put in evidence the phenomena described above, and to design a model inside the Matlab-Simulink environment, able to simulate the strategy and to make possible tests on the vehicle by means of a prototype.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

Development of a thermochemical/biological process to convert waste biomass into new resources

The increasing attention to environmental issues of recent times encourages us to find new methods for the production of energy from renewable sources, and to improve existing ones, increasing their energy yield. Most of the waste and agricultural residues, with a high content of lignin and non-hydrolysable polymers, cannot be effectively transformed into biofuels with existing technology. The purpose of the study was to develop a new thermochemical/ biological process (named Py-AD) for the valorization of scarcely biodegradable substances. A complete continuous prototype was design built and run for 1 year. This consists into a slow pyrolysis system coupled with two sequential digesters and showed to produce a clean pyrobiogas (a biogas with significant amount of C2-C3 hydrocarbons and residual CO/H2), biochar and bio-oil. Py-AD yielded 31.7% w/w biochar 32.5% w/w oil and 24.8% w/w pyrobiogas. The oil condensate obtained was fractionated in its aqueous and organic fraction (87% and 13% respectively). Subsequently, the anaerobic digestion of aqueous fraction was tested in a UASB reactor, for 180 days, in increasing organic loading rate (OLR). The maximum convertible concentration without undergoing instability phenomena and with complete degradation of pyrogenic chemicals was 1.25 gCOD L digester-1 d-1. The final yield of biomethane was equal to 40% of the theoretical yield and with a noticeable additional production equal to 20% of volatile fatty acids. The final results confirm that anaerobic digestion can be used as a useful tool for cleaning of slow pyrolysis products (both gas and condensable fraction) and the obtaining of relatively clean pyrobiogas that could be directly used in internal combustion engine.

Design of an innovative electric vehicle simulator for Charging Systems' End-of-Line Testing

The trend related to the turnover of internal combustion engine vehicles with EVs goes by the name of electrification. The push electrification experienced in the last decade is linked to the still ongoing evolution in power electronics technology for charging systems. This is the reason why an evolution in testing strategies and testing equipment is crucial too. The project this dissertation is based on concerns the investigation of a new EV simulator design. that optimizes the structure of the testing equipment used by the company who commissioned this work. Project requirements can be summarized in the following two points: space occupation reduction and parallel charging implementation. Some components were completely redesigned, and others were substituted with equivalent ones that could perform the same tasks. In this way it was possible to reduce the space occupation of the simulator, as well as to increase the efficiency of the testing device. Moreover, the possibility of conjugating different charging simulations could be investigated by parallelly launching two testing procedures on a unique machine, properly predisposed for supporting the two charging protocols used. On the back of the results achieved in the body of this dissertation, a new design for the EV simulator was proposed. In this way, space reduction was obtained, and space occupation efficiency was improved with the proposed new design. The testing device thus resulted to be way more compact, enabling to gain in safety and productivity, along with a 25% cost reduction. Furthermore, parallel charging was implemented in the proposed new design since the conducted tests clearly showed the feasibility of parallel charging sessions. The results presented in this work can thus be implemented to build the first prototype of the new EV simulator.

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

Analysis of the impact of stationary energy storage systems in trolleybus grids using Simulink-based modelling

The voltage profile of the catenary between traction substations (TSSs) is affected by the trolleybus current intake and by its position with respect to the TSSs: the higher the current requested by the bus and the further the bus from the TSSs, the deeper the voltage drop. When the voltage drops below 500V, the trolleybus is forced to decrease its consumption by reducing its input current. This thesis deals with the analysis of the improvements that the installation of an BESS produces in the operation of a particularly loaded FS of the DC trolleybus network of the city of Bologna. The stationary BESS is charged by the TSSs during off-peak times and delivers the stored energy when the catenary is overloaded alleviating the load on the TSSs and reducing the voltage drops. Only IMC buses are considered in the prospect of a future disposal of all internal combustion engine vehicles. These trolleybuses cause deeper voltage drops because they absorb enough current to power their traction motor and recharge the on board battery. The control of the BESS aims to keep the catenary voltage within the admissible voltage range and makes sure that all physical limitations are met. A model of FS Marconi Trento Trieste is implemented in Simulink environment to simulate its daily operation and compare the behavior of the trolleybus network with and without BESS. From the simulation without BESS, the best location of the energy storage system is deduced, and the battery control is tuned. Furthermore, from the knowledge of the load curve and the battery control trans-characteristic, it is formulated a prediction of the voltage distribution at BESS connection point. The prediction is then compared with the simulation results to validate the Simulink model. The BESS allows to decrease the voltage drops along the catenary, the Joule losses and the current delivered by the TSSs, indicating that the BESS can be a solution to improve the operation of the trolleybus network.

Analisi energetica al variare dello state of charge di propulsori ibridi e plug-in operanti nel ciclo WLTP con propulsore termico a punto fisso stechiometrico

Il presente lavoro propone la stesura di un codice in ambiente MATLAB per l'analisi energetica di powertrain ibridi operanti nel ciclo WLTP con il particolare funzionamento del motore termico benzina a punto fisso stechiometrico. Nello specifico, il codice prende dei dati in input tramite un'interfaccia grafica ed avvia una simulazione, i cui risultati principali corrispondono ad emissioni di anidride carbonica per chilometro percorso ed ai chilometri percorsi con un litro di carburante. Queste sono le due grandezze che questo lavoro si prefigge di ottimizzare, dalle quali proviene la scelta del particolare funzionamento del motore termico. Sono state implementate due differenti strategie di controllo, una generale ed una più particolare. La simulazione con la strategia generale è stata applicata a tre differenti scenari reali, con le caratteristiche tecniche di tre veicoli realmente esistenti che vengono inserite nell'interfaccia grafica. I risultati provenienti da queste simulazioni sono stati poi analizzati e discussi nel dettaglio, anche con l'ausilio di grafici a supporto della spiegazione. La strategia di controllo particolare non è stata applicata a scenari reali ma ha portato alla definizione di una ipotetica architettura di powertrain ibrido, alla quale la sopracitata strategia si adatta al meglio. Dopo aver proposto miglioramenti e possibili ulteriori sviluppi di questo lavoro, sono state tratte le conclusioni generali, allargando il tutto al contesto più ampio in cui si va ad inserire.

ICE Emissions Estimation through Machine-learning techniques to support real-world R&D experiments

The emissions estimation, both during homologation and standard driving, is one of the new challenges that automotive industries have to face. The new European and American regulation will allow a lower and lower quantity of Carbon Monoxide emission and will require that all the vehicles have to be able to monitor their own pollutants production. Since numerical models are too computationally expensive and approximated, new solutions based on Machine Learning are replacing standard techniques. In this project we considered a real V12 Internal Combustion Engine to propose a novel approach pushing Random Forests to generate meaningful prediction also in extreme cases (extrapolation, very high frequency peaks, noisy instrumentation etc.). The present work proposes also a data preprocessing pipeline for strongly unbalanced datasets and a reinterpretation of the regression problem as a classification problem in a logarithmic quantized domain. Results have been evaluated for two different models representing a pure interpolation scenario (more standard) and an extrapolation scenario, to test the out of bounds robustness of the model. The employed metrics take into account different aspects which can affect the homologation procedure, so the final analysis will focus on combining all the specific performances together to obtain the overall conclusions.